Summary of Bayesian Low-rank Learning (bella): a Practical Approach to Bayesian Neural Networks, by Bao Gia Doan et al.
Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks
by Bao Gia Doan, Afshar Shamsi, Xiao-Yu Guo, Arash Mohammadi, Hamid Alinejad-Rokny, Dino Sejdinovic, Damien Teney, Damith C. Ranasinghe, Ehsan Abbasnejad
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, called Bayesian Low-Rank LeArning (Bella), aims to mitigate the computational complexity of Bayesian learning in large-scale tasks. By leveraging low-rank perturbations of pre-trained neural network parameters, Bella significantly reduces the number of trainable parameters required to approximate a Bayesian posterior. This approach enables the seamless implementation of both vanilla and more sophisticated Bayesian learning methods, such as Stein Variational Gradient Descent (SVGD), in large models. The results demonstrate that Bella maintains or surpasses the performance of conventional Bayesian learning methods and non-Bayesian baselines on tasks like ImageNet, CAMELYON17, DomainNet, VQA with CLIP, and LLaVA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian learning can be useful for improving the robustness of artificial intelligence models. However, it has been limited by its high computational complexity. A new framework called Bella aims to make Bayesian learning more practical. Bella reduces the number of calculations needed to perform Bayesian learning by using low-rank perturbations of pre-trained neural network parameters. This allows larger models to be used for tasks like image recognition and natural language processing. |
Keywords
» Artificial intelligence » Gradient descent » Natural language processing » Neural network
